CN108982446A - A kind of inline diagnosis and the imaging-PAM system and method for being classified crop disease - Google Patents
A kind of inline diagnosis and the imaging-PAM system and method for being classified crop disease Download PDFInfo
- Publication number
- CN108982446A CN108982446A CN201810739176.7A CN201810739176A CN108982446A CN 108982446 A CN108982446 A CN 108982446A CN 201810739176 A CN201810739176 A CN 201810739176A CN 108982446 A CN108982446 A CN 108982446A
- Authority
- CN
- China
- Prior art keywords
- imaging
- crop
- pam
- room
- dark adaptation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/64—Fluorescence; Phosphorescence
Landscapes
- Health & Medical Sciences (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention discloses a kind of inline diagnosis and it is classified the imaging-PAM system and method for crop disease, comprising: dark adaptation unit, imaging-PAM unit and automatically deliver unit；Dark adaptation room is connected with imaging-PAM room, crop is from entering dark adaptation room, after completing dark adaptation；Imaging chamber is reached, plant can be transported in parallel measuring center position by article carrying platform, and the rotary table on article carrying platform can also carry out lifting and rotation process to plant according to plant height, until reaching suitable measurement position；Imaging-PAM room is measured in real time plant, settable experimental arrangement realizes automatic cycle measurement, imaging data is automatically credited computer and carries out online data analysis, computer according to the photochemistry quantum efficiency Fv/Fm in surveyed chlorophyll fluorescence kinetics parameters, photochemical quenching coefficient qP, whether these three parameter diagnosis crops of non-photochemical quenching coefficient qN susceptible and are classified to the disease of susceptible crop.
Description
Technical field
The present invention relates to crop imaging-PAM technology, in particular to a kind of inline diagnosis is simultaneously classified crop disease
Imaging-PAM system and method.
Background technique
Crop disease and insect is to influence crop quality and an important factor for yield, and crop disease and insect is often with having fulminant type, destroy
Property and the features such as sudden, are main biological epidemics in China's agricultural production.Corps diseases type is more, influences big, disaster
Property it is strong, while causing agricultural output and quality to decline, can also cause pesticide a large amount of investments and expenses for prevention and control it is upper
It rises, to increase agriculture production cost and cause serious environmental pollution.Therefore, the prevention and treatment of pest and disease damage is to maintain in crops
The leading factor of agricultural sustainable development.Catching an illness for crop is broadly divided into contact phase, stage of invasion, incubation period and period of disease, passes
System detection crop disease mainly has traditional technology diagnosis, the utilization of immune response and the method based on pathogen nucleic acid, respectively has excellent
It disadvantage but takes time and effort mostly, is complicated for operation, not being suitable for picking out suitable disease-resistant variety in large quantities of crops.With Sclerotina Sclerotiorum in Winter Rape
For core disease, crop Different Organs (root, stem, leaf), different growing stages (seedling stage, peduncle-growing period for rapeseed, florescence, silique phase) can be made
The experimental study of object disease resistance, the phenotype for picking out high disease resistance are most important to the breeding of crop later period.
Chlorophyll fluorescence phenomenon refers to that chlorophyll fluorescence first rapidly rises to by the plant of dark adaptation after illumination condition
One maximum value, is then gradually reduced, and finally reaches the process of a stationary value.Most variations of crop photosynthesis are ok
It is reflected, the probe that chlorophyll fluorescence is studied as photosynthesis, is had specific, high sensitivity by chlorophyll fluorescence
Feature can quickly reflect plant physiological ecology situation, and can be realized non-destructive testing, mutant strain screening, pest and disease damage detection,
The various fields such as phenotypic analysis, which suffer from, to be widely applied.But it waits too long presently, there are dark adaptation time, cannot achieve automatic
The problems such as sample introduction and disease scale and on-line monitoring, therefore be badly in need of one kind for inline diagnosis crop disease and disease point can be carried out
The imaging-PAM system and device of grade.
In order to make up the deficiency of original technology means, the on-line detecting system, one of advantage be by dark adaptation room at
As room is connected, the drawbacks of conventional method is needed crop batch dark treatment manually is overcome, by the dark adaptation processing stream of crop
Aquation, the blade under dark adaptation state, PSII (Photosystem I I) are in complete open state, and the blade after abundant dark adaptation is used
Measure minimum fluorescence Fo after faint modulation measurement light irradiation, then with saturated light flash irradiation blade, PSII is completely closed, surveyed at this time
The difference for obtaining maximum fluorescence Fm, Fm and Fo is Fv, and Fv/Fm is the sub- efficiency of the photochemically reactive maximum amount of PSII.Normal plants
The Fv/Fm of blade is 0.82-0.84, and the photosynthesis performance of susceptible crop declines, and PSII vigor reduces, Fv/Fm, qP and qN etc.
Parameter is below normal plant, therefore can divide according to the variation of these chlorophyll fluorescence kinetics parameters the disease of crop
Grade has a major impact the early diagnosis of disease to the screening and breeding of high disease-resistant crop.
Summary of the invention
The invention discloses inline diagnosis and it is classified the imaging-PAM system and method for crop disease, realizes base
In inline diagnosis and classification of the imaging-PAM system to crop disease.This system not only solves traditional detection method and needs
Very important person is dark adaptation to be handled streamlined, and be provided with automatic elevating rotary station the drawbacks of crop is shifted to an earlier date dark treatment,
Can be according to the highly automated adjusting of crop to suitable measurement position, the computer being connected with sensor can be according to preset program
Realize automatic cycle measurement.This system has high degree of automation, detects the features such as quick, lossless and applied widely.
System is suitable for but is not limited to the Rapid&Early diagnosis of the typical fungus diseases such as rice, rape, Solanaceae class vegetables.Particular technique side
Case is as follows:
A kind of inline diagnosis and the imaging-PAM system and method for being classified crop disease, comprising:
1) crop is communicated successively reaches in the dark adaptation room being connected with imaging-PAM room with system, dark from entering
It adapts to room to rise, until detection finishes, for whole process under dark condition, crop needs 20min before entering imaging-PAM room
Dark adaptation processing, each imaging-PAM needs 210s, and dark adaptation room accommodates six basin crops every time, enters at first dark suitable
Answer the crop of room after the dark adaptation by 20min, into imaging-PAM room, while there have a basin crop to enter again to be dark suitable
Room is answered, is circuited sequentially, dark adaptation is handled into streamlined；
2) after crop reaches imaging-PAM room, crop is transported to measuring center position by article carrying platform in parallel, is carried
Rotary table on object platform can also carry out lifting and rotation process to plant according to plant height, until reaching suitable measurement position
It sets；
3) imaging-PAM room is measured in real time plant, and setting experimental arrangement realizes automatic cycle measurement, journey
Measurement light in sequence first to the crop irradiation 5s for terminating dark adaptation, obtains dark adaptation minimum fluorescence F0, then irradiates to crop
Continue the saturation color break-up of 800ms, obtain dark adaptation maximum fluorescence Fm, irradiates the actinic light of 70s finally to obtain Kautsky
Inductive effect maximum fluorescence Fp, all imaging datas are automatically credited computer and carry out online data analysis, extract crop
The parameters such as photochemistry quantum efficiency Fv/Fm, photochemical quenching coefficient qP, non-photochemical quenching coefficient qN divide crop disease
Grade；
4) step 1)~step 3) is repeated, the examining online to crop disease using imaging-PAM technology can be realized
Disconnected and classification.
A kind of inline diagnosis and the imaging-PAM system and method for being classified crop disease, including delivery module, letter
Cease acquisition module and result output module.The delivery module mainly includes the manipulator of conveyer belt and crawl crop.Conveyer belt
Through entire dark adaptation room and imaging chamber, it is responsible for crop being transported into dark adaptation room until reaching imaging chamber, and realize whole system
Automatic cycle detection.
The information acquisition module mainly includes dark adaptation room and imaging-PAM room, and crop is from entering dark adaptation room
It rises, communicated band is slowly transported to imaging chamber, the time of the controllable fabrication dark adaptation of speed by adjusting conveyer belt, overcomes
Traditional detection gimmick needs the drawbacks of crop is artificially shifted to an earlier date dark adaptation processing.The imaging chamber mainly includes 4 LED light source boards
It can provide plurality of stable light source, a CCD camera lens, a sensor being connected with computer and the lifting platform for placing crop,
Lifting platform can carry out rotation and descending operation according to the height of crop, and system has high scalability, can also configure the according to demand
Five light source boards are placed in the top of imaging chamber.
Preferably, two pairs of LEDs light source boards can provide measurement light, photochemical light and saturation color break-up, according to demand can
Excite different combination of light sources and UV ultraviolet source that can excite multiwave fluorescence imaging analysis, the angle of two pairs of LED light source boards
Degree and height can be adjusted according to plant.
Preferably, CCD camera lens is installed in the top of imaging chamber with sensor and is connected directly with computer, in real time
It acquires and analyzes data.
The result treatment module includes mainly the computer for handling crop chlorophyll fluorescence information, has editable leaf
Green element fluorescence experiments program, including snapshot mode, Fv/Fm, Kautsky inductive effect, 2 chlorophyll fluorescence quenching assays
(NPQ) test procedure etc. can carry out repeating imaging measurement analysis automatically, and data result is stored in computer.This example is main
According to photochemistry quantum efficiency Fv/Fm, the variation of photochemical quenching coefficient qP and non-photochemical quenching coefficient qN these three parameters
Crop disease is diagnosed and is classified.
Detailed description of the invention
Fig. 1 is inline diagnosis and the imaging-PAM system and method for being classified crop disease；
Fig. 2 is chlorophyll fluorescence kinetics curve of this example by taking rape as an example；
Fig. 3 is crop disease diagnosis and stage division flow chart.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the invention.
As shown in Figure 1, inline diagnosis in the present invention and to be classified the imaging-PAM system of crop disease include: machine
Tool hand 1, computer 2, dark adaptation room 3, conveyer belt 4, imaging-PAM room 5, sensor 6, LED light source board 7, CCD camera lens 8
With sample lifting platform 9.
For manipulator 1 by crop from being transferred on conveyer belt 4 in greenhouse, communicated band 4 is transported to the progress of dark adaptation room 3
After the dark adaptation of a period of time, continuation is transported to the imaging-PAM room 5 being connected with dark adaptation room 3 by conveyer belt 4, i.e., in fact
The on-line checking means that dark adaptation processing carries out simultaneously with imaging-PAM are showed, crop reaches imaging-PAM room
After 5 by sample lifting platform 9 by crop lifting, rotate to suitable measurement position, two pairs of LED light source boards 7 provide measurement light, photochemical
Light and saturation color break-up are learned, is equably radiated on crop sample, angle and height are adjustable, and 8 height of CCD camera lens is also adjustable, imaging
Area reaches as high as 35 × 35cm, and sensor 6 is connected with computer 2, by computer real-time acquisition and analyzes data.Whole system
It may be implemented repeatedly to recycle measurement, there is the crop of obvious high disease resistance to remove detection queue by computer controlled machine tool hand.It is whole
A device is applicable to the other plants such as plant leaf blade and fruit tissue, whole plant or the plurality of plants of culture, moss lichens
Equal rudimentary plants, algae etc..
As shown in Figures 2 and 3, detection method includes the following steps for the present embodiment:
1) crop is communicated successively reaches in the dark adaptation room being connected with imaging-PAM room with system, dark from entering
It adapts to room to rise, until detection finishes, for whole process under dark condition, crop needs 20min before entering imaging-PAM room
Dark adaptation processing, each imaging-PAM needs 210s, and dark adaptation room accommodates six basin crops every time, enters at first dark suitable
Answer the crop of room after the dark adaptation by 20min, into imaging-PAM room, while there have a basin crop to enter again to be dark suitable
Room is answered, is circuited sequentially, dark adaptation is handled into streamlined；
2) after crop reaches imaging-PAM room, crop is transported to measuring center position by article carrying platform in parallel, is carried
Rotary table on object platform can also carry out lifting and rotation process to plant according to plant height, until reaching suitable measurement position
It sets；
3) imaging-PAM room is measured in real time plant, and setting experimental arrangement realizes automatic cycle measurement, journey
Measurement light in sequence first to the crop irradiation 5s for terminating dark adaptation, obtains dark adaptation minimum fluorescence F0, then irradiates to crop
Continue the saturation color break-up of 800ms, obtain dark adaptation maximum fluorescence Fm, irradiates the actinic light of 70s finally to obtain Kautsky
Inductive effect maximum fluorescence Fp, all imaging datas are automatically credited computer and carry out online data analysis, extract crop
The parameters such as photochemistry quantum efficiency Fv/Fm, photochemical quenching coefficient qP, non-photochemical quenching coefficient qN divide crop disease
Grade；
4) step 1)~step 3) is repeated, the examining online to crop disease using imaging-PAM technology can be realized
Disconnected and classification.
Claims (7)
1. a kind of inline diagnosis and the imaging-PAM system for being classified crop disease characterized by comprising dark adaptation list
Member and automatically delivers unit at imaging-PAM unit；
The dark adaptation unit is made of dark adaptation room, and the imaging-PAM unit is by imaging-PAM room structure
At, the unit that automatically delivers is made of the conveyer belt system and manipulator through dark adaptation room and imaging-PAM room,
Dark adaptation room is used to provide the dark adaptation environment before chlorophyll fluorescence measurement for crop, with the direct phase in imaging-PAM room
Even, both ends are respectively equipped with the closed induction door of black, crop from dark adaptation unit reach the process of imaging-PAM unit by
Conveyer belt system is completed.
2. inline diagnosis as described in claim 1 and the imaging-PAM system for being classified crop disease, which is characterized in that
Dark adaptation room is set up directly on the conveyer belt that one section is connected with imaging-PAM system, and whole process is under dark condition,
Crop can accordingly be adjusted from the time that dark adaptation room reaches imaging chamber according to the length of time of measuring, dark adaptation room and imaging
Room is connected.
3. inline diagnosis as described in claim 1 and the imaging-PAM system for being classified crop disease, which is characterized in that
Imaging-PAM unit uses modular construction, including two pairs of LEDs light source boards, CCD camera lens, bracket, control unit, also
Light source board can be assembled to be placed at the top of imaging chamber.
4. inline diagnosis as claimed in claim 3 and the imaging-PAM system for being classified crop disease, which is characterized in that
Two pairs of LEDs light source boards can provide measurement light, photochemical light and saturation color break-up, can excite different combination of light sources according to demand,
And UV ultraviolet source can excite multiwave fluorescence imaging analysis, the angle and height of two pairs of LED light source boards can be according to plants
Strain is adjusted.
5. inline diagnosis as described in claim 1 and the imaging-PAM system for being classified crop disease, which is characterized in that
CCD camera lens is installed in the top of imaging chamber with sensor and is connected directly with computer, acquires in real time and analyzes data.
6. a kind of inline diagnosis and the imaging-PAM method for being classified crop disease, based on any one of claim 1-5 institute
The imaging-PAM system stated is realized, which comprises the following steps:
1) crop is communicated successively reaches in the dark adaptation room being connected with imaging-PAM room with system, from enter dark adaptation
Room is risen, until detection finishes, for whole process under dark condition, crop needs dark adaptation to handle before entering imaging-PAM room
Afterwards, into imaging-PAM room, while there is a basin crop to enter dark adaptation room again, circuit sequentially, by dark adaptation processing stream
Aquation；
2) after crop reaches imaging-PAM room, crop is transported to measuring center position by article carrying platform in parallel, and loading is flat
Rotary table on platform can also carry out lifting and rotation process to plant according to plant height, until reaching suitable measurement position；
3) imaging-PAM room is measured in real time plant, and setting experimental arrangement realizes automatic cycle measurement, right first
The crop irradiation measurement light for terminating dark adaptation, obtains dark adaptation minimum fluorescence F0, color break-up then is saturated to crop prolonged exposure, is obtained
Dark adaptation maximum fluorescence Fm must be obtained, irradiates actinic light finally to obtain Kautsky inductive effect maximum fluorescence Fp, all imagings
Data are automatically credited computer and carry out online data analysis, extract the photochemistry quantum efficiency Fv/Fm of crop, photochemistry is quenched
It goes out coefficient qP and non-photochemical quenching coefficient qN parameter is classified crop disease；
4) repeat step 1)~step 3), can be realized using imaging-PAM technology to the inline diagnosis of crop disease with
Classification.
7. inline diagnosis as claimed in claim 6 and the imaging-PAM method for being classified crop disease, which is characterized in that
The PSII of susceptible crop is affected, and Fv/Fm, qP and qN are below normal crop, by extracting crop imaging-PAM
The parameters such as Fv/Fm, qP and qN can directly diagnose crop disease and the susceptible degree of crop is classified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810739176.7A CN108982446A (en) | 2018-07-06 | 2018-07-06 | A kind of inline diagnosis and the imaging-PAM system and method for being classified crop disease |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810739176.7A CN108982446A (en) | 2018-07-06 | 2018-07-06 | A kind of inline diagnosis and the imaging-PAM system and method for being classified crop disease |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108982446A true CN108982446A (en) | 2018-12-11 |
Family
ID=64537305
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810739176.7A Pending CN108982446A (en) | 2018-07-06 | 2018-07-06 | A kind of inline diagnosis and the imaging-PAM system and method for being classified crop disease |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108982446A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115839937A (en) * | 2022-12-20 | 2023-03-24 | 江苏省中国科学院植物研究所 | Taxus chinensis stress detection method based on chlorophyll fluorescence imaging technology |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102010009119A1 (en) * | 2010-02-24 | 2011-08-25 | Schowe, Lisa, 48155 | Method for determining photosynthesis activity of plant, involves determining chlorophyll fluorescence level of plant based on chlorophyll concentration of leaf to detect light adaptation state of plant |
| CN105784651A (en) * | 2016-03-04 | 2016-07-20 | 中国科学院植物研究所 | Plant leaf cross section maximum photochemical quantum efficiency determinator and application method |
| CN106546568A (en) * | 2016-10-31 | 2017-03-29 | 浙江大学 | A kind of method and device for obtaining plant three-dimensional chlorophyll fluorescence image information |
| CN106896077A (en) * | 2017-04-28 | 2017-06-27 | 浙江大学 | The detection method of the transgenic corns glyphosate tolerant phenotype based on imaging-PAM |
| CN107132228A (en) * | 2017-06-06 | 2017-09-05 | 浙江大学 | A kind of high flux phenotype research system of the full growth period information of rape |
-
2018
- 2018-07-06 CN CN201810739176.7A patent/CN108982446A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102010009119A1 (en) * | 2010-02-24 | 2011-08-25 | Schowe, Lisa, 48155 | Method for determining photosynthesis activity of plant, involves determining chlorophyll fluorescence level of plant based on chlorophyll concentration of leaf to detect light adaptation state of plant |
| CN105784651A (en) * | 2016-03-04 | 2016-07-20 | 中国科学院植物研究所 | Plant leaf cross section maximum photochemical quantum efficiency determinator and application method |
| CN106546568A (en) * | 2016-10-31 | 2017-03-29 | 浙江大学 | A kind of method and device for obtaining plant three-dimensional chlorophyll fluorescence image information |
| CN106896077A (en) * | 2017-04-28 | 2017-06-27 | 浙江大学 | The detection method of the transgenic corns glyphosate tolerant phenotype based on imaging-PAM |
| CN107132228A (en) * | 2017-06-06 | 2017-09-05 | 浙江大学 | A kind of high flux phenotype research system of the full growth period information of rape |
Non-Patent Citations (1)
| Title |
|---|
| 杨志晓等: "赤星病胁迫对不同抗性烟草品种光合作用和叶绿素荧光特性的影响", 《生态学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115839937A (en) * | 2022-12-20 | 2023-03-24 | 江苏省中国科学院植物研究所 | Taxus chinensis stress detection method based on chlorophyll fluorescence imaging technology |
| CN115839937B (en) * | 2022-12-20 | 2024-01-09 | 江苏省中国科学院植物研究所 | Taxus chinensis stress detection method based on chlorophyll fluorescence imaging technology |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105717115B (en) | High-throughput Plant phenotypic analysis device and method based on optical image technology | |
| KR101341815B1 (en) | Screening devices of seeds using hyperspectral image processing | |
| US20120018356A1 (en) | Title of invention method and device for determining plant material quality using images containing information about the quantum efficiency and the time response of the photosynthtic system | |
| EP1563282B1 (en) | A method and a device for making images of the quantum efficiency of the photosynthetic system with the purpose of determining the quality of plant material and a method for classifying and sorting plant material | |
| CN107132228A (en) | A kind of high flux phenotype research system of the full growth period information of rape | |
| DE19845883A1 (en) | Assembly for automatic bio tests | |
| CN102841072A (en) | Method for identifying transgenic rice and non-transgenic rice based on NIR (Near Infrared Spectrum) | |
| CN110554017A (en) | Method and device for correcting fluorescence parameters of plant chlorophyll | |
| Lancelot et al. | Near-infrared hyperspectral imaging for following imbibition of single wheat kernel sections | |
| CN106546569B (en) | A kind of screening technique and device of the plant drought resistance mutant of high throughput | |
| Keränen et al. | Automatic plant identification with chlorophyll fluorescence fingerprinting | |
| CN108982446A (en) | A kind of inline diagnosis and the imaging-PAM system and method for being classified crop disease | |
| CN101074925A (en) | Method for diagnosing plant-leaf botrytis in visible and near-infrared spectral characteristic band | |
| US20190346419A1 (en) | Method and apparatus for measuring inflorescence, seed and/or seed yield phenotype | |
| CN105203467B (en) | Universal and nondestructive growth fitting method for fungi | |
| CN114136920A (en) | Hyperspectrum-based single-grain hybrid rice seed variety identification method | |
| KR20160056361A (en) | Drought tolerance index derived from chlorophyll fluorescence parameters in Maize(Zea Mays L.) | |
| CN111802281B (en) | Optical fiber spectrum grading detection device and method for fertilized eggs in pre-incubated breeding duck eggs | |
| CN110806404A (en) | Improved chlorophyll fluorescence detection method and device | |
| CN116151454A (en) | Method and system for predicting yield of short-forest linalool essential oil by multispectral unmanned aerial vehicle | |
| CN1804590A (en) | Method and apparatus for monitoring crop growth regulation and control | |
| CN102788796A (en) | Nutrient diagnosis device and nutrient diagnosis method for nitrogen of crops based on multi-information integration of high spectral images and fluorescent images | |
| CN115728249A (en) | Prediction method for chlorophyll content of tomato seedlings and processing terminal | |
| CN112179881B (en) | Early detection method for mung bean leaf spot based on chlorophyll fluorescence imaging | |
| CN207215710U (en) | A kind of high flux phenotype research system of the full growth period information of rape |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181211 |
|
| RJ01 | Rejection of invention patent application after publication |